Statlog (German Credit Data) Dataset

Statlog (German Credit Data) Dataset

In this article, we analyze the UCI Statlog (german credit data) from Kaggle.com.

Context

The original dataset contains 1000 entries with 20 categorial/symbolic attributes prepared by Prof. Hofmann. In this dataset, each entry represents a person who takes a credit by a bank. Each person is classified as good or bad credit risks according to the set of attributes. The link to the original dataset can be found below.

Content

It is almost impossible to understand the original dataset due to its complicated system of categories and symbols. Thus, I wrote a small Python script to convert it into a readable CSV file. Several columns are simply ignored, because in my opinion either they are not important or their descriptions are obscure. The selected attributes are:

Preprocessing

Dealing with NaN values.

Modifications

Age

Checking Account

$$\mbox{Checking Account} = \begin{cases} 0, &\mbox{None},\\ 1, &\mbox{Little},\\ 2, &\mbox{Moderate},\\ 3, &\mbox{Rich}. \end{cases}$$

Credit Amount

Duration

Housing

$$\mbox{Housing} = \begin{cases} 0, &\mbox{Free},\\ 1, &\mbox{Rent},\\ 2, &\mbox{Own,} \end{cases}$$

Job

Purpose

Risk

$$\mbox{Risk} = \begin{cases} 0, &\mbox{Bad},\\ 1, &\mbox{Good}. \end{cases}$$

Saving Accounts

$$\mbox{Saving Accounts} = \begin{cases} 0, &\mbox{None},\\ 1, &\mbox{Little},\\ 2, &\mbox{Moderate},\\ 3, &\mbox{Quite Rich},\\ 4, &\mbox{Rich}. \end{cases}$$

Sex

$$\mbox{Sex} = \begin{cases} 0, &\mbox{Female},\\ 1, &\mbox{Male}. \end{cases}$$

Features with high variance

Moreover, high variance for some features can hurt our modeling process. For this reason, we would like to standardize features by removing the mean and scaling to unit variance.

Data Correlations


References

  1. Kaggle German Credit Risk Dataset